🏡 House Price Prediction (Beginner → Intermediate Project)

This project predicts house prices using Machine Learning (Linear Regression and Random Forest).
It demonstrates a complete Data Science workflow — from data analysis to model evaluation.

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🎯 Project Objective

To build a predictive model that estimates house prices based on features like:

• Area (sq. ft)
• Number of Bedrooms
• Number of Bathrooms
• City
• Furnishing type
• Parking availability

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🧩 Steps Covered

1. Importing Libraries – Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn
2. Data Creation/Loading – Sample dataset with features
3. Data Cleaning & Understanding – info(), describe(), missing values
4. EDA (Exploratory Data Analysis) – Heatmap, Pairplot, Scatterplots
5. Data Splitting – Train & Test sets
6. Feature Scaling – StandardScaler
7. Model Training – Linear Regression & Random Forest
8. Model Evaluation – R² Score, MSE, RMSE comparison
9. Feature Importance – Which features influence price most
10. Model Saving – Export trained model using joblib

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📈 Results Summary

Model	R² Score	MSE
Linear Regression	0.9538	275,793,442
Random Forest	0.9834	98,688,840

✅ Random Forest performed best
🎯 Top Features: area, bedrooms

🧠 Learning Outcomes

• Understanding regression algorithms
• Data preprocessing and scaling
• Model evaluation metrics (R², MSE)
• Comparing ML models
• Visualizing feature importance

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⚙️ Tech Stack

• Python 🐍
• Pandas, NumPy
• Matplotlib, Seaborn
• Scikit-learn
• Joblib

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▶️ How to Run

1. Clone this repo

   git clone https://github.com/<your-username>/House-Price-Prediction.git
   

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📊 Feature Importance (Example Output)